If you’ve been in the software game long enough then you can recount days or weeks tracking down a particularly tricky bug that was resolved by a single line of code.

That’s because software engineering is not about the amount of code you write: it’s about problem solving.

So then logically, either: - there are problems still to solve and you can work to solve them (maybe with AI) - there are no problems. So let’s head to the beach, as there are no problems.

I get a neuronal immune response to bureaucracy

It seems like AGI until it does something that leaves you scratching your head. Getting a simple thing wrong.

Double precision cell chips were reserved for military, I think (or maybe also blade processors). So doing any serious physics on them was dead in the water - same with macs tbh

PS3 had not the same Cell chips as the sones for data centers.

> doing Typescript type fixes with this model across 50 files cost me $54 this afternoon.

Not trying to be harsh, but that sounds like a skill issue. You have the language server to lean on; easy feedback loop; sub agent per type.

A fascinating insight into the architecture of modern cpus from an entrepreneurial angle. Bravo!

I wonder if we could gamify and democratise it somehow, like fold-at-home and wikipedia...

I've been training a teeny specialised model to run in a browser on a phone to detect harmonium notes played in a song (harmonium turns out is a pita, another story for another day), getting good labelled data is _all_ of the hard work.

That being said, maybe for cheap inference, using a big model to train something ultra-suited for the task at hand might be how we could handle local inference; thinking language specific models.

Before customisations

The price of convenience.

But you forgot about Vickers